WILLEM AI ECG registry for adults at high risk of heart disease

Registry Study for the Evaluation of High-risk Cardiac Patients by WILLEM AI-based ECG Platform

This registry will test whether the WILLEM AI can read ECGs to detect cardiac abnormalities in adults at high risk for heart disease using real-world hospital and wearable recordings.

Quick facts

• Study type: Observational
• Enrollment: 200000 (estimated)
• Ages: 18 Years and up
• Sex: All
• Sponsor: Idoven 1903 S.L. (Industry-sponsored)
• Locations: 4 sites (Nashville, Tennessee and 3 other locations)
• Trial ID: NCT07333547 on ClinicalTrials.gov

What this trial studies

This is a large, single-group observational registry that will collect both retrospective and prospective ECGs and linked clinical data to generate real-world evidence on WILLEM's performance. The platform will process raw ECG tracings from a range of devices (12-lead, Holters, wearables, patches, telemetry, etc.) that meet minimum duration and sampling requirements. Sites will submit de-identified ECG raw data and associated clinical information so WILLEM's automated interpretations can be compared with clinical findings and local standards of care. The study focuses on adults admitted to cardiovascular units who are considered high risk for cardiac disease across participating hospitals in the US and Spain.

Who should consider this trial

Good fit: Ideal candidates are adults (over 18) at high risk for cardiac disease who have at least one legible raw ECG tracing from any supported device and available clinical data.

Not a fit: Patients without usable raw ECG data, children under 18, or people not classified as high cardiac risk are unlikely to benefit from participation.

Why it matters

Potential benefit: If successful, WILLEM could speed up ECG interpretation, improve early detection of cardiac abnormalities, and reduce the workload and costs for cardiology services.

How similar studies have performed: Previous clinical trials of WILLEM have shown promising ECG interpretation performance, but broader real-world registry evidence remains limited.

Eligibility criteria

Inclusion Criteria:

* EC/IRB approval of ICF waiver prior to recruitment; otherwise, signed informed consent form by subject and investigator
* Age \> 18 years-old, with no upper limit
* Subjects undergoing standard of care electrocardiogram (ECG) of any duration from any hardware device
* All available, but at least one, legible ECG tracings in raw data format (e.g. DICOM, XML, EDF, JSON, HL7, SCP, WFDB, CSV, etc.)
* Available subject clinical data associated with the ECG
* For 12-lead ECGs, a minimum length of 10 seconds at a minimum sample frequency of 250 Hz
* For ECGs from Holters, wearables, patches, insertable cardiac monitors, telemetries, etc., a minimum length of 30 seconds at a minimum sample frequency of 200 Hz with a lead I / II or its MCL-DII lead approximation
* For prospective eligibility only:
* Signed informed consent form, unless previously waived by the EC/IRB
* Site technical viability for ECG and subject clinical data transfer (e.g. end-to-end integration following interoperability standards such as FHIR, HL7 or DICOM)

Exclusion Criteria:

* Unavailable or suboptimal quality of the raw data from the ECG signal
* Age \< 18 years-old

Where this trial is running

Nashville, Tennessee and 3 other locations

• Vanderbilt University Medical Center — Nashville, Tennessee, United States (Recruiting)
• La Paz University Hospital — Madrid, Spain (Not_yet_recruiting)
• Puerta de Hierro University Hospital — Madrid, Spain (Not_yet_recruiting)
• Murcia University — Murcia, Spain (Not_yet_recruiting)

Study contacts

• Study coordinator: Manuel Marina-Breysse, MD, PhD

Conditions High-risk Cardiac Patients; artificial intelligence; electrocardiogram; deep learning; cardiac disease; registry